Title :
A self-organizing network for computing a posteriori conditional class probability
Author :
Rogers, George W. ; Solka, Jeffrey ; Malyevac, Stephen D. ; Priebe, Carey E.
Author_Institution :
US Naval Surface Warfare Center, Dahlgren, VA, USA
Abstract :
A neural network architecture whose goal is the computation of a posteriori conditional class probabilities for input vectors that belong to one of two input classes is described. The network architecture has been designed to adaptively produce Voronoi tessellation partitions of the input vectors in Rn based on the Euclidean distance metric, without regard to the actual a priori class probabilities of the input vectors. These prior probabilities are then used by the network to adaptively compute the a posteriori conditional class probability for the two classes for each tessellation partition. The network presented is thus a connectionist model for vector quantization clustering and includes the process of automatic node creation necessary for many unsupervised learning applications
Keywords :
computational geometry; pattern recognition; probability; self-organising feature maps; vector quantisation; Euclidean distance metric; Voronoi tessellation partitions; a posteriori conditional class probability; automatic node creation; connectionist model; neural network architecture; self-organizing network; unsupervised learning; vector quantization clustering; Computer architecture; Computer networks; Euclidean distance; Neural networks; Pattern analysis; Pattern recognition; Probability distribution; Self-organizing networks; Unsupervised learning; Vector quantization;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on
